%0 Conference Paper %1 Zhou_2020 %A Zhou, Guojing %A Yang, Xi %A Azizsoltani, Hamoon %A Barnes, Tiffany %A Chi, Min %B Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization %D 2020 %I ACM %K personalized-learning sequencing umap2020 %R 10.1145/3340631.3394848 %T Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies %U https://doi.org/10.1145%2F3340631.3394848 %X Motivated by the recent advances of reinforcement learning and the traditional grounded Self Determination Theory (SDT), we explored the impact of hierarchical reinforcement learning (HRL) induced pedagogical policies and data-driven explanations of the HRL-induced policies on student experience in an Intelligent Tutoring System (ITS). We explored their impacts first independently and then jointly. Overall our results showed that 1) the HRL induced policies could significantly improve students' learning performance, and 2) explaining the tutor's decisions to students through data-driven explanations could improve the student-system interaction in terms of students' engagement and autonomy.

@inproceedings{Zhou_2020, abstract = {Motivated by the recent advances of reinforcement learning and the traditional grounded Self Determination Theory (SDT), we explored the impact of hierarchical reinforcement learning (HRL) induced pedagogical policies and data-driven explanations of the HRL-induced policies on student experience in an Intelligent Tutoring System (ITS). We explored their impacts first independently and then jointly. Overall our results showed that 1) the HRL induced policies could significantly improve students' learning performance, and 2) explaining the tutor's decisions to students through data-driven explanations could improve the student-system interaction in terms of students' engagement and autonomy.}, added-at = {2020-07-15T02:32:47.000+0200}, author = {Zhou, Guojing and Yang, Xi and Azizsoltani, Hamoon and Barnes, Tiffany and Chi, Min}, biburl = {https://www.bibsonomy.org/bibtex/26019d1af1190c563d15efc0355f34fa3/brusilovsky}, booktitle = {Proceedings of the 28th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies | Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3340631.3394848}, interhash = {dd313e39193cc26ddd677c80df9851d2}, intrahash = {6019d1af1190c563d15efc0355f34fa3}, keywords = {personalized-learning sequencing umap2020}, month = jul, publisher = {{ACM}}, timestamp = {2020-09-02T17:19:24.000+0200}, title = {Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies}, url = {https://doi.org/10.1145%2F3340631.3394848}, year = 2020 }